Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false; the description adds context by specifying deterministic JSON, the exact schema version, and the single-call pipeline. It does not contradict the annotations, and it clarifies the operation is non-mutating and repeatable.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The entire description is one sentence that front-loads the primary action and keeps all details necessary. It avoids filler and each clause adds distinct information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a three-stage tool, the description explains the pipeline at a high level, and the output schema covers return details. It does not discuss failure modes or the effect of root_mode/max_errors, but those are partially described in the input schema, making this adequate but not exhaustive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers 67% of parameters with descriptions (format, instance, root_mode, check_formats), and the description complements them by linking format/content to the normalization step. However, content and max_errors remain without extra explanation, and the description doesn't add syntax or edge-case details beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses specific verbs (normalize, infer, validate) and names concrete formats (CSV, TSV, JSON, JSON Lines, YAML) plus a schema version (Draft 2020-12). It clearly distinguishes this from sibling data tools by framing a combined pipeline, with 'validate one future JSON value' as the unique deliverable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies a use case (processing structured data into a contract and checking a future value) but does not explicitly state when to prefer this over data.convert, data.schema, or data.schema-validate, nor does it mention exclusions. Without explicit alternatives or when-not-to-use, guidance remains only implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.